title: Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods.
abstract: The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by "batch effects," the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
authors.affiliation: National Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, People's Republic of China.
authors.forename: Chao
authors.initials: C
authors.lastname: Chen
authors.forename: Kay
authors.initials: K
authors.lastname: Grennan
authors.forename: Judith
authors.initials: J
authors.lastname: Badner
authors.forename: Dandan
authors.initials: D
authors.lastname: Zhang
authors.forename: Elliot
authors.initials: E
authors.lastname: Gershon
authors.forename: Li
authors.initials: L
authors.lastname: Jin
authors.forename: Chunyu
authors.initials: C
authors.lastname: Liu
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D046228:Microarray Analysis / Q000592:standards / Q000706:statistics & numerical data*; D012372:ROC Curve; D012015:Reference Standards; D012107:Research Design; D018401:Sample Size; D015983:Selection Bias; D054928:Validation Studies as Topic